Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

In today's scenario, there are frequent events (viz. political rallies, live concerts, strikes,
sports meet) occur in which many people gather to participate in the event. In crowded areas …

We present an extension to the disjoint paths problem in which additional lifted edges are
introduced to provide path connectivity priors. We call the resulting optimization problem the …

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging
the representational power of deep learning to jointly learn to detect and track objects …

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential
in a commercial application. It aims to predict multiple pedestrian trajectories and maintain …

In multi-object tracking, the tracker maintains in its memory the appearance and motion
information for each object in the scene. This memory is utilized for finding matches between …

This work addresses the unsupervised adaptation of an existing object detector to a new
target domain. We assume that a large number of unlabeled videos from this domain are …

For a long time, the most common paradigm in MultiObject Tracking was tracking-by-
detection (TbD), where objects are first detected and then associated over video frames. For …